View/Open

Metadata

Abstract

The ``winning'' system in the 2013 MIREX Latin Genre Classification Task was a deep neural network trained with simple features. An explanation for its winning performance has yet to be found. In previous work, we built similar systems using the {\em BALLROOM} music dataset, and found their performances to be greatly affected by slightly changing the tempo of the music of a test recording. In the MIREX task, however, systems are trained and tested using the {\em Latin Music Dataset (LMD)}, which is 4.5 times larger than {\em BALLROOM}, and which does not seem to show as strong a relationship between tempo and label as {\em BALLROOM}. In this paper, we reproduce the ``winning'' deep learning system using {\em LMD}, and measure the effects of time dilation on its performance. We find that tempo changes of at most $\pm 6$\% greatly diminish and improve its performance. Interpreted with the low-level nature of the input features, this supports the conclusion that the system is exploiting some low-level absolute time characteristics to reproduce ground truth in {\em LMD}.